Nrf2 Inhibitor, Brusatol in Combination with Trastuzumab Exerts Synergistic Antitumor Activity in HER2-Positive Cancers by Inhibiting Nrf2/HO-1 and HER2-AKT/ERK1/2 Pathways
Bibliographic record
Abstract
The HER2-targeting antibody trastuzumab has shown effectiveness in treating HER2-positive breast and gastric cancers; however, its responses are limited. Currently, Nrf2 has been deemed as a key transcription factor in promoting cancer progression and resistance by crosstalk with other proliferative signaling pathways. Brusatol as a novel Nrf2 inhibitor has been deemed as an efficacious and safe drug candidate in cancer therapy. In this study, we firstly reported that brusatol exerted the growth-inhibitory effects on HER2-positive cancer cells by regressing Nrf2/HO-1 and HER2-AKT/ERK1/2 signaling pathways in these cells. More importantly, we found that brusatol synergistically enhanced the antitumor activity of trastuzumab against HER2-positive SK-OV-3 and BT-474 cells, which may be attributed to the inhibition of Nrf2/HO-1 and HER2-AKT/ERK1/2 signaling pathways. Furthermore, the synergistic effects were also observed in BT-474 and SK-OV-3 tumor xenografts. In addition, our results showed that trastuzumab markedly enhanced brusatol-induced ROS accumulation and apoptosis level, which could further explain the synergistic effects. To conclude, the study provided a new insight on exploring Nrf2 inhibition in combination with HER2-targeted trastuzumab as a potential clinical treatment regimen in treating HER2-positive cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".